Devic mouse: a spontaneous double-transgenic mouse model of human opticospinal multiple sclerosis and autoimmune T- B cell cooperation

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system (CNS). Myelin antigen(s) specific T cells, B cells, and antibodies are thought to play a role in the pathogenesis of MS. While the influence of autoantigenspecific CD4+ T cells has been extensively studied in animal models, the relevance of autoantigen specific B cells and their interactions with pathogenic T cells are largely unknown. The original aim of the present study was to create a new mouse model with which to investigate the interaction of myelin autoantigen specific B and T cells and their role in MS pathogenesis. The study was further expanded to analyze the nature and triggers of spontaneous disease and similarity of the mouse lesion pattern to that in human disease. The double-transgenic mouse (“Devic mouse”) strain presented here contains myelin oligodendrocyte glycoprotein (MOG)-specific T as well as B cells. A significant proportion (>50%) of these mice showed spontaneous experimentalautoimmune encephalomyelitis (EAE)-like disease at a young age. In contrast, all single transgenic littermates were free of clinical disease. Spontaneous EAE requires both MOG-specific T and B cells, since the breeding of MOG-specific Ig heavy chain knock-in mice with ovalbumin specific T cell receptor (TCR) transgenic mice did not develop any disease. Histological analysis of the CNS of affected mice revealed restricted localization of lesions in the spinal cord and optic nerves as well as severe demyelination and axonal damage that spared brain and cerebellum. The inflammatory infiltrates were predominantly composed of macrophages and CD4+ T cells, but occasionally also eosinophils. This peculiar localization of the demyelinating lesions and infiltration profile differ from classic EAE and is reminiscent of Devic’s neuromyelitis optica, a variant of classic MS in humans. It is not well understood what triggers the initiation of spontaneous EAE. The microbial environment does not significantly affect the clinical disease. Stimulation of the innate immune system with toll-like receptor (TLR) ligands or depletion of putative regulatory cells did not significantly affect EAE development. The (re-)activation of lymphocytes in sick Devic mice mainly occurs in the CNS without evidence of priming in the peripheral lymphoid organs. MOG-specific B and T cells cooperate by means of several mechanisms. MOGspecific B cells, which bind MOG but not the immunodominant peptide MOG 35-55 via their surface immunoglobulin (Ig), efficiently presented even high dilutions of MOG to T cells. This resulted in the enhanced proliferation of T and B cells as well as rapid activation. Stimulated T, but not B cells, secreted large amounts of Th1 cytokines IFNg and IL-2 along with small amounts of Th2 cytokine IL-5. In addition, MOG-stimulated T and B cells expressed a set of co-stimulatory molecules, which further help to modulate the proliferation and activation. Surprisingly, the doubletransgenic Devic mice, but not their single transgenic littermates, had high titers of MOG-specific IgG1 antibodies in the serum, which indicates a previous encounter with antigen in vivo. However, similar MOG-specific serum IgG1 titers were present irrespective of the clinical status. The transfer of EAE by Devic splenocytes in immunodeficient mice or by bone marrow reconstitution in wild-type mice further supported the in vivo cooperation of MOG-specific T and B cells to induce spontaneous EAE. In summary, Devic mice show several salient features that are important for study of the pathogenic mechanisms of CNS autoimmunity. As a model of spontaneous autoimmunity, they may allow us to study the triggering factors of autoimmunity as well as the factors that determine restricted infiltration of immune cells into the CNS.In addition, the model may be useful for validating novel therapies for autoimmune CNS diseases

Efficient Data Representation by Selecting Prototypes with Importance Weights

Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract. In this paper we present algorithms with strong theoretical guarantees to mine these data sets and select prototypes a.k.a. representatives that optimally describes them. Our work notably generalizes the recent work by Kim et al. (2016) where in addition to selecting prototypes, we also associate non-negative weights which are indicative of their importance. This extension provides a single coherent framework under which both prototypes and criticisms (i.e. outliers) can be found. Furthermore, our framework works for any symmetric positive definite kernel thus addressing one of the key open questions laid out in Kim et al. (2016). By establishing that our objective function enjoys a key property of that of weak submodularity, we present a fast ProtoDash algorithm and also derive approximation guarantees for the same. We demonstrate the efficacy of our method on diverse domains such as retail, digit recognition (MNIST) and on publicly available 40 health questionnaires obtained from the Center for Disease Control (CDC) website maintained by the US Dept. of Health. We validate the results quantitatively as well as qualitatively based on expert feedback and recently published scientific studies on public health, thus showcasing the power of our technique in providing actionability (for retail), utility (for MNIST) and insight (on CDC datasets) which arguably are the hallmarks of an effective data mining method.Comment: Accepted for publication in International Conference on Data Mining (ICDM) 201

Signal Recovery in Perturbed Fourier Compressed Sensing

In many applications in compressed sensing, the measurement matrix is a Fourier matrix, i.e., it measures the Fourier transform of the underlying signal at some specified `base' frequencies $\{u_i\}_{i=1}^M$, where $M$ is the number of measurements. However due to system calibration errors, the system may measure the Fourier transform at frequencies $\{u_i + \delta_i\}_{i=1}^M$ that are different from the base frequencies and where $\{\delta_i\}_{i=1}^M$ are unknown. Ignoring perturbations of this nature can lead to major errors in signal recovery. In this paper, we present a simple but effective alternating minimization algorithm to recover the perturbations in the frequencies \emph{in situ} with the signal, which we assume is sparse or compressible in some known basis. In many cases, the perturbations $\{\delta_i\}_{i=1}^M$ can be expressed in terms of a small number of unique parameters $P \ll M$. We demonstrate that in such cases, the method leads to excellent quality results that are several times better than baseline algorithms (which are based on existing off-grid methods in the recent literature on direction of arrival (DOA) estimation, modified to suit the computational problem in this paper). Our results are also robust to noise in the measurement values. We also provide theoretical results for (1) the convergence of our algorithm, and (2) the uniqueness of its solution under some restrictions.Comment: New theortical results about uniqueness and convergence now included. More challenging experiments now include